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. 2018 Oct 18;175(3):615-632.
doi: 10.1016/j.cell.2018.09.010.

Stem Cells, Genome Editing, and the Path to Translational Medicine

Free PMC article

Stem Cells, Genome Editing, and the Path to Translational Medicine

Frank Soldner et al. Cell. .
Free PMC article


The derivation of human embryonic stem cells (hESCs) and the stunning discovery that somatic cells can be reprogrammed into human induced pluripotent stem cells (hiPSCs) holds the promise to revolutionize biomedical research and regenerative medicine. In this Review, we focus on disorders of the central nervous system and explore how advances in human pluripotent stem cells (hPSCs) coincide with evolutions in genome engineering and genomic technologies to provide realistic opportunities to tackle some of the most devastating complex disorders.


Figure 1:
Figure 1:. Comparison of hiPSC-based strategies to model complex diseases in cell culture
A. HiPSC-based strategies to identify diseases-associated phenotypes for monogenetic diseases comparing (i) the conventional approach with (ii) an isogenic approach. Genome editing allows to generate an experimental system with controlled genetic background by either correcting a disease-associated mutation in patient-derived cells or inversely inserting the mutation into wild-type hPSCs. B. HiPSC-based strategies to model sporadic diseases comparing (i) the conventional approach with (ii) a patient stratification approach based on known disease associated genotypes (risk score or specific risk genes) or clinical phenotypes and (iii) population genetics approaches to identify associations between specific genotypes and QTLs and (iv) the functional analysis of specific disease-disease associated risk variants. Genome editing allows to generate an allelic series of isogenic cell lines carrying distinct risk variants.
Figure 2:
Figure 2:. Identification of functional risk variants based on epigenetic signatures
Candidate disease-associated risk variants (e.g.SNP) are prioritized based on integrating GWAS data with a variety of epigenetic datasets. Risk variants overlapping with tissue-specific regulatory elements are more likely to be functional compared to risk variants outside of regulatory elements. Epigenetic datasets can be generated either from primary tissue or from in vitro hPSC-derived somatic cell types. Schema at the bottom depicts (i) the identification of a hypothetical GWAS identified diseases-associated risk variant overlapping with a brain-specific distal enhancer (indicated by enrichment for histone H3 lysine 27 acetylation Chip-seq signal), (ii) the CRISPR/Cas9 mediated SNP exchange and (iii) the functional analysis of the SNP exchange indicated by a genotype-dependent cis-regulatory effect on expression of Gene Y (eQTL).
Figure 3:
Figure 3:. Comparison of in vitro and chimeric in vivo approaches to model complex disease-associated phenotypes
hPSCs, human pluripotent stem cells; NPs, neural progenitors; TF, transcription factor.

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